Description

Book Synopsis

Machine Learning and Deep Learning Applications.- Optimizing Blood Donation Operations with a PHP Driven Management System.- FIT FORMA: live fitness tracking application with blaze pose.- Genetic Algorithm Driven Hyperparameter Optimization for Precision Sickle Cell Disease Prognosis.- Enhanced Weed Classification with a Custom CNN: Evaluating Optimizer Performance.- A STRATEGY FOR SAFEGUARDING THE MOST FRAGILE RAIL TRANSPORT SYSTEM IN THE EVENT OF AN ACCIDENT - “TRACK SENTIAL”.- Leveraging Transfer Learning for Plant Identification with Limited Data: A MobileNet V2 Based Approach.- Airline Passenger Satisfaction Analysis using Classifiers of Computation Models and Explainable AI.- Classify Object Behavior to Enhance the Safety of Autonomous Vehicles.- Bottom Bounce Analysis of Multipath Acoustic Propagation in Deep Water.- Comparative Analysis of Self-Supervised Monocular Depth Estimation and ORB-SLAM2 in Visual Perception and Robotics.- Acoustic based Drone Detection using Machine Learning.- Bifunctional Tunable Metasurface for Terahertz Shielding and Refractive Index Sensing Using a Machine Learning Based Prediction Model.- Temporal Analysis in Deep Learning-Based Recommender Systems for Predicting Plant Disease Outbreaks.- Enhancing IoT Security: Multiclass Traffic Classification with Advanced Machine Learning Algorithms.- Detection of Artificial Profiles on Social Media Platforms Using Machine Learning and Natural Language Processing.- Towards sustainable weed management using lightweight deep learning model.- Enhancing Safety in Autonomous Vehicles Using Advanced Deep Learning-Based Pothole Detection.- CalMeter: Food Recognition and Calorie Estimation using Deep Learning.- Enhancing Web-Based Advanced Persistent Threat Detection through Deep Learning Techniques.- Application of Machine Learning Algorithms for Vegetable Quality Prediction.- Diabetic Retinopathy Detection using Deep Learning.- Optimized Feature Selection for Motion Sensor Based Human Activity Recognition with Machine Learning Techniques.- Enhancing Spectrum Awareness in Cellular Networks through Deep Learning Approaches for Efficient 5G-NR and LTE Signal Classification.- Advancements in Diabetes Prediction: Integrating Machine Learning with Smart Sensor.- A Hybrid Deep Learning Approach to X-Ray Diagnosis of Lung Diseases.- Transparency in Lung Cancer Prediction: Integrating Explainable AI Techniques with Machine Learning Models.- Accurate Prediction and Classification of Heart failure using Machine learning algorithms and Interpretation using Explainable AI.- Electric Vehicle Charging Stations: A Comparative Study of Multiple Machine Learning Classifiers and Interpretation Using Explainable AI.- Deep Learning for Urban Sound Classification: Using CNN and YAMNet Model Integration.- MelanomaNet: Deep Learning for Skin Cancer Diagnosis through Inception V3.- Deciphering Chronic Kidney Disease Diagnosis: A Comparative Exploration of Computational Approaches with LIME and SHAP Interpretation.- Exploring Seed Quality Assessment through Convolutional Neural Networks and Generative Adversarial Networks.- Tuberculosis Detection in Chest X-rays using Deep Learning Algorithms with segmentation and data augmentation techniques.

Advanced Technologies in Electronics Communications and Signal Processing

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    A Paperback by Krishna Kishore Koganti

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      Publisher: Springer
      Publication Date: Publication Date: 17/07/2025
      ISBN13: 9783031942822, 978-3031942822
      ISBN10:
      Also in:
      Computing Network hardware

      Description

      Book Synopsis

      Machine Learning and Deep Learning Applications.- Optimizing Blood Donation Operations with a PHP Driven Management System.- FIT FORMA: live fitness tracking application with blaze pose.- Genetic Algorithm Driven Hyperparameter Optimization for Precision Sickle Cell Disease Prognosis.- Enhanced Weed Classification with a Custom CNN: Evaluating Optimizer Performance.- A STRATEGY FOR SAFEGUARDING THE MOST FRAGILE RAIL TRANSPORT SYSTEM IN THE EVENT OF AN ACCIDENT - “TRACK SENTIAL”.- Leveraging Transfer Learning for Plant Identification with Limited Data: A MobileNet V2 Based Approach.- Airline Passenger Satisfaction Analysis using Classifiers of Computation Models and Explainable AI.- Classify Object Behavior to Enhance the Safety of Autonomous Vehicles.- Bottom Bounce Analysis of Multipath Acoustic Propagation in Deep Water.- Comparative Analysis of Self-Supervised Monocular Depth Estimation and ORB-SLAM2 in Visual Perception and Robotics.- Acoustic based Drone Detection using Machine Learning.- Bifunctional Tunable Metasurface for Terahertz Shielding and Refractive Index Sensing Using a Machine Learning Based Prediction Model.- Temporal Analysis in Deep Learning-Based Recommender Systems for Predicting Plant Disease Outbreaks.- Enhancing IoT Security: Multiclass Traffic Classification with Advanced Machine Learning Algorithms.- Detection of Artificial Profiles on Social Media Platforms Using Machine Learning and Natural Language Processing.- Towards sustainable weed management using lightweight deep learning model.- Enhancing Safety in Autonomous Vehicles Using Advanced Deep Learning-Based Pothole Detection.- CalMeter: Food Recognition and Calorie Estimation using Deep Learning.- Enhancing Web-Based Advanced Persistent Threat Detection through Deep Learning Techniques.- Application of Machine Learning Algorithms for Vegetable Quality Prediction.- Diabetic Retinopathy Detection using Deep Learning.- Optimized Feature Selection for Motion Sensor Based Human Activity Recognition with Machine Learning Techniques.- Enhancing Spectrum Awareness in Cellular Networks through Deep Learning Approaches for Efficient 5G-NR and LTE Signal Classification.- Advancements in Diabetes Prediction: Integrating Machine Learning with Smart Sensor.- A Hybrid Deep Learning Approach to X-Ray Diagnosis of Lung Diseases.- Transparency in Lung Cancer Prediction: Integrating Explainable AI Techniques with Machine Learning Models.- Accurate Prediction and Classification of Heart failure using Machine learning algorithms and Interpretation using Explainable AI.- Electric Vehicle Charging Stations: A Comparative Study of Multiple Machine Learning Classifiers and Interpretation Using Explainable AI.- Deep Learning for Urban Sound Classification: Using CNN and YAMNet Model Integration.- MelanomaNet: Deep Learning for Skin Cancer Diagnosis through Inception V3.- Deciphering Chronic Kidney Disease Diagnosis: A Comparative Exploration of Computational Approaches with LIME and SHAP Interpretation.- Exploring Seed Quality Assessment through Convolutional Neural Networks and Generative Adversarial Networks.- Tuberculosis Detection in Chest X-rays using Deep Learning Algorithms with segmentation and data augmentation techniques.

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